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基于支持向量機的道路坡度實時預(yù)測方法試驗
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國家自然科學(xué)基金資助項目(51105001)和汽車安全與節(jié)能國家重點實驗室開放基金資助項目(KF14022)


Experimental Research on Real-time Prediction Method for Road Slope Based on Support Vector Machine
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    摘要:

    道路坡度預(yù)測是汽車ABS、AMT、混合動力汽車扭矩分配等實時控制的關(guān)鍵技術(shù)。提出一種基于支持向量機(SVM)的道路坡度實時預(yù)測方法,輸入?yún)?shù)為發(fā)動機轉(zhuǎn)速、輸出扭矩、縱向車速和縱向加速度,均從控制器CAN網(wǎng)絡(luò)中實時提取。分別構(gòu)建實車道路試驗系統(tǒng)和CarSim仿真平臺,通過系統(tǒng)試驗分別得到的樣本對SVM模型進行學(xué)習(xí)和泛化能力測試。結(jié)果表明:CarSim試驗數(shù)據(jù)建立的SVM模型預(yù)測平方相關(guān)系數(shù)達到0.99,實車試驗數(shù)據(jù)建立的SVM模型預(yù)測平方相關(guān)系數(shù)在0.9左右,二者差異的主要原因是實車試驗GPS方法獲取道路坡度信息時疊加了不易消除的車體俯仰角的影響。基于LabVIEW編程將實車試驗SVM模型導(dǎo)入虛擬儀器PXIe實時控制器中,其預(yù)測一個點的耗時等效到汽車電控ECU單片機為1.33ms,完全滿足實時控制要求。證明所提出道路坡度預(yù)測方法是有效、可行的。

    Abstract:

    Prediction of road slope is a key technology to vehicles’ electronic real-time control system, such as ABS, AMT and hybrid torque distribution, and so on. In this paper, a real-time prediction method of road slope was put forward based on support vector machine (SVM), in which the input parameters of SVM module included engine speed, engine output torque, vehicle speed and longitudinal acceleration, and could be extracted from controller CAN network in real-time. The vehicle roadway test system and the CarSim simulation platform were built up respectively, and the samples required for SVM model learning, generalization performance test were achieved by the systematic tests. The squared correlation coefficient of SVM model from CarSim tests was 0.99, while it was 0.9 from roadway tests. The main reason for the difference could be that the GPS method in road slope test may add in a body pitch angle which could not be eliminated systematically. Furthermore, the SVM model of roadway test was imported into the real time virtual controller PXIe using LabVIEW programming method. For the equivalent prediction time of one point to the single chip computer selected by automotive electronic controller was only 1.33ms , which met the requirements of real time control . The road slope prediction method proposed in this paper is effective and practicable.

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張小龍,陳 彬,宋 健,潘 登.基于支持向量機的道路坡度實時預(yù)測方法試驗[J].農(nóng)業(yè)機械學(xué)報,2014,45(11):14-19. Zhang Xiaolong, Chen Bin, Song Jian, Pan Deng. Experimental Research on Real-time Prediction Method for Road Slope Based on Support Vector Machine[J]. Transactions of the Chinese Society for Agricultural Machinery,2014,45(11):14-19.

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  • 收稿日期:2014-05-26
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  • 在線發(fā)布日期: 2014-11-10
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